GroupBy & The Split-Apply-Combine Pattern

Group aggregations with agg(), custom lambda summaries, and transform()

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The Split-Apply-Combine paradigm groups rows by categorical keys, applies statistical aggregations or transformations, and combines results into a new summary DataFrame.

The agg() and transform() Powerhouses

Split: df.groupby("department") groups rows sharing identical department values.

Apply & Combine: Calling .agg({"salary": ["mean", "median"], "bonus": "sum"}) calculates distinct aggregations for each column simultaneously.

transform(): Applies an aggregation across groups but broadcasts the result back to the original DataFrame row count, allowing row-level comparisons against group averages.

Multi-Metric GroupBy Aggregation

Try It Yourself »

import pandas as pd

sales = pd.DataFrame({
    "region": ["East", "West", "East", "West", "East", "West"],
    "rep": ["Alice", "Bob", "Charlie", "Diana", "Evan", "Frank"],
    "revenue": [5000, 7200, 4800, 8100, 6200, 6900]
})

# Summary report per region
report = sales.groupby("region").agg(
    total_revenue=("revenue", "sum"),
    average_revenue=("revenue", "mean"),
    rep_count=("rep", "count")
)
print("Regional Performance Summary:
", report)

Named aggregations create clean, readable summary reports.

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Test Yourself With Exercise

+15 XP

What is the main difference between `groupby.agg()` and `groupby.transform()`?

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